Papers by Yazheng Yang
VLFeedback: A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment (2024.emnlp-main)
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Lei Li, Zhihui Xie, Mukai Li, Shunian Chen, Peiyi Wang, Liang Chen, Yazheng Yang, Benyou Wang, Lingpeng Kong, Qi Liu
| Challenge: | Large vision-language models (LVLMs) are evolving rapidly and require data with human supervision to achieve better alignment. |
| Approach: | They introduce VLFeedback, the first large-scale vision-language feedback dataset . they train Silkie, an LVLM fine-tuned via direct preference optimization . |
| Outcome: | The proposed model outperforms its base model in helpfulness, visual faithfulness, and safety metrics and exhibits enhanced resilience against red-teaming attacks. |
Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference (P18-1)
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| Challenge: | Existing approaches to natural language inference focus on interaction architectures of sentences . but, we propose to transfer knowledge from discourse markers to augment the model . |
| Approach: | They propose to transfer knowledge from discourse markers to augment the quality of the NLI model. |
| Outcome: | The proposed method achieves state-of-the-art performance on large-scale datasets. |
MPII: Multi-Level Mutual Promotion for Inference and Interpretation (2022.acl-long)
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| Challenge: | Existing methods for providing interpretations provide human-unfriendly interpretations, resulting in sub-optimal performance. |
| Approach: | They propose a multi-level Mutual Promotion mechanism for self-evolved inference and sentence-level interpretation that integrates inference with interpretation in an autoregressive manner. |
| Outcome: | The proposed approach outperforms baseline models on NLI and CQA tasks for both inference performance and interpretation quality. |
Cognitive Alpha Mining via LLM-Driven Code-Based Evolution (2026.acl-long)
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| Challenge: | Existing approaches to finding effective predictive signals from financial data are limited by their complexity and low signal-to-noise ratio. |
| Approach: | They propose a framework that combines code-level alpha representation with LLM-driven reasoning and evolutionary search. |
| Outcome: | The proposed framework combines code-level alpha representation with LLM-driven reasoning and evolutionary search. |